Learning to Translate with Source and Target Syntax

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How researchers see this paper

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Graph Convolutional Encoders for Syntax-aware Neural Machine Translation (2017, neutral): “This goal is in line with claims that rigid syntactic constraints typically hurt MT (Zollmann and Venugopal, 2006; Smith and Eisner, 2006; Chiang, 2010), and, though these claims have been made in the context of traditional MT systems, we believe they are no less valid for NMT. Attention-based NMT…”
Tuning as Ranking (2011, methodology): “, 2010), only one paper describing a statistical MT system cited the use of MIRA for tuning (Chiang, 2010), while 15 used MERT.”
Hope and Fear for Discriminative Training of Statistical Translation Models (2012, methodology): “, 2008b) and used it successfully with many different kinds of features (Chiang et al., 2009; Chiang, 2010; Chiang et al., 2011).”
Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features (2014, background): “Subsequent research showed that substantial performance gains can be achieved if hard constraints— specifically, isomorphism between a source sentence’s parse and the parse of its translation—are relaxed (Liu, Lü, and Liu 2009; Chiang 2010; Zhang, Zhai, and Zong 2011; Hanneman and Lavie 2011).”